Feed.FM And The Soundtrack Of Spending

When most people think of the music that tends to accompany shopping, their first association is probably Muzak — and it probably isn’t wholly positive.

“Muzak – bringing bad music to an elevator near you,” Feed.FM CEO Jeff Yasuda noted of Muzak’s long and illustrious reputation.

And while it is easy to write Muzak off as the place one hears all of todays best pop songs – as interpreted by Kenny G – it is also easy to forget two things, Yasuda said.  Muzak, as we tend to associate it, is more or less a relic, it was bought out by Mood Media in 2011 for $345 million.  Also, Muzak garnered a $345 million price tag for a reason – which is that Muzak got very good at matching the background music to the commerce experience.

“This concept has been around for decades in brick-and-mortar retail – powering music in restaurants and stores.  What music makes a customer move an experience faster, what encourages people to stay around and browse, what types of music make people buy more – there is a lot of neurological science behind it.”

And while the concept has been well-established in real-world shopping, Yasuda noted, Feed.FM is focused on bringing the same background music experience to the digital consumer.

“CMOs are spending 300 billion a year – text and blogs, spend on video, spend on video and picture. We think they should be spending on music because the largest age group in the U.S right now are young adults, and they are way over-indexed on music consumption.”

And – he noted, given the share price performance of many of retails bigger players of late – and the increasing atmosphere that its time to do something different or risk not being around to do anything at all  – Feed.FM offers brands and merchants an opportunity to try something old – in a new, and more effective way.

Keeping Users Engaged 

While all brands are not created equal and all face different challenges, at the end of they day, the common challenge they all face is keeping users engaged.  And the online merchant that’s an extension of a brick-and-mortar player has something of an advantage in that arena, in that it can make its engagement much more customized to the individual user.

“In the digital world, we know what songs people like, what they dislike, what they skip, what is playing when conversions are made, and what is playing when people leave the pap. We can use data to create a better consumer experience – and one that the consumer can enjoy simultaneous to a commerce experience because music is the only medium that you can enjoy while you do something else.”

And that, Yasuda notes, allows Feed.FM to helps it partner brands move from curated models of music presentation – where the brand selects the 100 or so songs that represent the customer group – to a personalized model where what the consumer has on in the background reflects the preferences they have actively and passively shown in the past.

“If we feed you a song by a band you don’t like – the user is going to have a lousy experience.  My job isn’t to be the purveyor of cool, it is to create an experience for each customer that works for them.”

And what works for one customer won’t work for every customer – there is no one music genre that universally makes customers shop.  There are, trends – some bands are what Yasuda called “barbells,” meaning they attract no neutral feelings, consumers either love them or hate them.  Then there are artists that don’t quite have the extra-ordinary highs or lows – but manage to generate general enthusiasm.

And, mostly, he notes, it depends on the brand and the shopper.  Some brands like edgier music because they market to younger consumers – others, like Toys R Us, aren’t really looking for a lot of edge for any part of their consumer base.

There is, he noted, one exception to the rule of everyone’s individual taste being different.

“Oh my God, Drake. When we put Drake in the playlist, everybody loves Drake. It’s at the point that it is a joke in the industry.”

What’s Next 

For Feed.FM, Yasuda noted, the goal is to delight consumers – and make it a lot easier for merchants to delight consumers, too.  Music is both a good avenue for that, because of the type of medium it is, and how much data it generates.

It is also good for them, because it allows for a big value-add on the merchant’s end, using copyrighted material on their site without having to directly deal with the copyright holder.

“Music is something we know well. We handle all the licensing complexity and believe me there is a lot. Most marketers and product folks have been smart enough to say that they can’t use music without a license unless they want to have a very painful conversation with a copyright holder.”

And that service – and the access to musical content it provides, Yasuda noted, is useful across a variety of verticals, which is why Feed.FM works with sporting teams like the Golden State Warriors, or fitness clients.

“Which has a commerce element to it because these companies are selling essentially their version for a great workout. And 95 percent of the people that exercise are listening to some form of music.”

The goal is always to keep the user or customer engaged – and music, Yasuda noted, is a very powerful engagement tool.   Retail reinvention, he noted, doesn’t always have to be about inventing something new.  Sometimes, the goal is to take something that has worked very well in the past – even something as simple as background music – and making it worker better in a different and more digital retail landscape.

 

No Silver Bullet: Governance Under Spotlight as AI Shapes Payments Innovation

In an era of rapid technological evolution, few industries are as poised for transformation as banking and payments. It is a transformation that’s already underway, with technologies like generative artificial intelligence (GenAI).

“Governance, cost and utility form the three points of the [GenAI] triangle we’re trying to balance. Getting it right will unlock transformative possibilities for our industry,” Mark Sundt, CTO at Stax Payments, said during a conversation for the PYMNTS Series “What’s Next in Payments: Memo To The GenAI Companies.”

As banking and payments companies consider the technology, the focus is shifting toward practical applications of AI that can drive efficiencies, improve customer experiences and address fraud.

With AI tools embedded in nearly every software service, companies must also prioritize guardrails to ensure data privacy and security.

While AI’s potential is undeniable, Sundt highlighted critical concerns around safety and cost. “Governance has been our first priority,” he said. “Every service we use has some AI component embedded in it. Governance is about ensuring that these tools don’t inadvertently proliferate sensitive data.”

Challenges to AI Payment Applications

Cost considerations around AI usage are equally pressing. Large language models (LLMs), while powerful, can be prohibitively expensive to operate, and their utility doesn’t always justify the investment.

But as the industry shifts away from monolithic models that attempt to solve every problem, smaller, specialized “agentic models” are gaining traction. These models are designed to address specific issues efficiently and cost-effectively, marking a shift in how AI is deployed.

“We’re moving away from the kitchen-sink approach of large models to focused, orchestrated agents that solve specific problems effectively and at lower costs,” Sundt said.

This trend, he added, is particularly evident in customer service, where AI is being used to diagnose and resolve issues with precision. Instead of building massive models that attempt to answer every conceivable question, firms are developing smaller models focused on solving tangible problems.

“Customers don’t want encyclopedic answers. They want to know how to configure their terminal to optimize interchange fees and response times. That’s where smaller, specialized models shine,” Sundt said.

“This pivot toward dynamic, smaller models is akin to the evolution of computing itself,” he added, noting that it parallels the broader transition in technology from mainframe computing to distributed cloud applications.

AI Fraud Detection, Risk Management

AI’s role in risk management and fraud detection is another area of immense potential.

“The biggest red flags we encounter are merchants with newly established banking relationships or websites. These temporal attributes often signal fraudulent intent,” he said. Stax leverages AI to enrich decision-making by evaluating factors like account longevity, storefront existence and transactional behavior.

Sundt also described suspicious patterns in transactional fraud, such as large transactions followed by batch reversals or refunds issued to different credit cards. “These scenarios demand robust AI systems to detect and mitigate fraudulent activities at scale,” he said.

Looking ahead, Sundt believes the next frontier for AI lies in reasoning. 

“Current models excel at summarization and categorization, but they struggle with reasoning. Humans make decisions with incomplete information all the time. Teaching AI to do the same would be a monumental leap,” he said.

This capability would enhance everything from fraud detection to customer service, enabling AI to navigate complex, ambiguous scenarios with greater sophistication.

Still, Sundt underscored the need for AI providers to align their rapid development cycles with the slower, compliance-driven nature of the banking industry. 

“The financial sector doesn’t move at the speed of tech. It’s crucial for AI companies to understand our industry’s unique challenges and design solutions accordingly,” he said.